
We use a batch size of 1. Each large crop contains many large "patches". Since our network is a fully convolutional network, each large "patch" is a single training example.

We use ADAM optimizer [1]} with a learning rate of \(2 \times 10^{-4}\) , reduced to \(2 \times 10^{-5}\)  after \(100K\)  iterations.

We set the diffusion timesteps \(T = 50\) . This allows for fast sampling, without sacrificing image/video quality (This trade-off is simpler in our case because of the simplicity of our learned data distribution).

When generating diverse videos, we use the DDPM frame Projector to correct predicted frames by noising and denoising \(T_{corr}=3\)  steps.

We compared several noise schedules for the diffusion models and ended up using linear noise schedule (\(\beta _0=2\times 10^{-3}, \beta _T=0.4\) ) for single-image DDPM and cosine noise schedule [2]} for single-video DDPM.

Our standard network architecture consists of 16 ConvNext [3]} blocks, each block with a base dimension of 64 channels.

